Siemens Healthcare Gmbh

Guillaume Daval Frerot

Mariappan S Nadar

Peter Speier

Boris Mailhe

Mathias Nittka

Xiao Chen

Simon Arberet

For machine training and application of a trained complex-valued machine learning model, an activation function of the machine learning model, such as a neural network, includes a learnable parameter that is complex or defined in a complex domain with two dimensions, such as real and imaginary or magnitude and phase dimensions. The complex learnable parameter is trained for any of various applications, such as MR fingerprinting, other medical imaging, or non-medical uses.

Complex-valued neural network with learnable non-linearities in medical imaging

Machine training a network for and use of the machine-trained network are provided for tissue parameter estimation for a magnetic scanner using magnetic resonance fingerprinting. The machine-trained network is trained to both reconstruct a fingerprint image or fingerprint and to estimate values for multiple tissue parameters in magnetic resonance fingerprinting. The reconstruction of the fingerprint image or fingerprint may reduce noise, such as aliasing, allowing for more accurate estimation of the values of the multiple tissue parameters from the under sampled magnetic resonance fingerprinting information.

Magnetic resonance fingerprinting image reconstruction and tissue parameter estimation

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Lawrenceville, NJ, United States of America

Guillaume Daval Frerot